"Every expert was once a beginner who refused to skip the fundamentals."
Tensor, Fundamentals-Obsessed AI Agent
This is where the book begins. You arrive with Python, curiosity, and (we assume) some prior exposure to machine learning. Everything before this chapter is the front matter that told you what the book covers, who it is for, and how to read it. From here on, every chapter builds: by the end of Part I you will have written a working Transformer; by the end of the book you will have shipped an agent into production.
Chapter Overview
This chapter is your launchpad. Before we can understand how Large Language Models work, we need to build a solid foundation in machine learning, neural networks, and the tools we will use throughout the book. Think of this chapter as ensuring everyone speaks the same language before the real journey begins, from NLP fundamentals (Chapter 01) all the way through to building AI agents (Chapter 26).
We start with the core ideas of machine learning: how machines learn patterns from data, what can go wrong (overfitting), and how to fix it. Then we dive into neural networks and the magic of backpropagation, concepts you will see again when we study the Transformer architecture (Chapter 3). Next, we get our hands dirty with PyTorch, the framework that powers most modern LLM research and development. Finally, we introduce reinforcement learning, the paradigm that makes LLMs helpful through RLHF, a topic explored in full in Chapter 18: Alignment, RLHF & DPO.
PyTorch is the lingua franca of modern LLM engineering. Nearly every model you will use in this book was trained in it, every fine-tuning library wraps it, and every production inference server can ingest its checkpoints. This chapter brings classical ML and PyTorch under one roof so that subsequent chapters can focus on what makes LLMs different rather than re-explaining backpropagation. The investment pays off across every Part that follows.
- Explain supervised learning, loss functions, and gradient descent intuitively and mathematically (these resurface in Chapter 16: Fine-Tuning Fundamentals)
- Describe the bias-variance tradeoff and apply regularization techniques
- Build and train neural networks, understanding backpropagation at a mechanical level
- Write complete PyTorch training loops with custom datasets and GPU acceleration, skills applied throughout Part 4: Training & Adapting
- Explain the RL framework (agent, policy, reward) and its connection to LLM training via RLHF and DPO (Chapter 18)
Prerequisites
- Python proficiency (functions, classes, list comprehensions, decorators)
- Basic linear algebra: vectors, matrices, dot products
- Basic probability: distributions, expectation, Bayes' theorem
- No prior ML experience required
Sections
- 0.1 What Every LLM Engineer Needs From Classical ML Why do we need ML basics for an LLM book? Entry
- 0.2 Deep Learning Essentials This section builds directly on the ML fundamentals covered in Section 0.2: ML Basics, particularly cross-entropy, loss functions, and the bias-variance tradeoff. Intermediate
- 0.3 PyTorch Tensors, Autograd & Training Loop PyTorch tensors, autograd, nn.Module, data loading, the basic training loop, and saving/loading model state. Advanced
- 0.4 PyTorch Debugging, Lab & Modern Performance Debugging with hooks and profilers, common mistakes, a FashionMNIST lab, and modern PyTorch features (torch.compile, AMP, FSDP). Advanced
- 0.5 Reinforcement Learning Foundations Why RL in an LLM book? Entry
- 0.5a PPO, RLHF Pipeline & the Full RL-to-LLM Alignment Picture PPO clipping, the bridge from RL to LLM training, and an end-to-end RLHF walkthrough. Intermediate
What's Next?
Next: Chapter 1: Foundations of NLP & Text Representation. You now know how a neural network learns from data via gradient descent. The next chapter answers a question that gradient descent alone cannot: how do you turn the word "cat" into something a tensor operation can chew on? The answer (vector representations of text) is what makes every LLM in this book possible.